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clem 🤗@ClementDelangueAI score35AIClément Delangue of Hugging Face shared a link to a Hugging Face model page for mistralai/Mistral-Large-4.0-1T05-A52B. The post itself gives no further details about the model's capabilities, release terms, or benchmarks.
LM Studio@lmstudioAI score44AILM Studio posted "We're so back" with no further details in the main post. Quoted context from Mistral AI says Mistral Large 4 has 1T parameters, 49B active, is natively multimodal, and is available via API today, with open weights planned for end of October.
Yuchen Jin@Yuchenj_UWAI score72AIMistral announced Mistral Large 4, a natively multimodal model with 1T parameters and 49B active, available via API today. Mistral claims it is the best open weights model from the US or Europe on aggregated benchmarks, with open weights set for release at the end of October. The author quotes this claim and comments that it appears to beat GLM-5.3.
AIMistral AI's Mistral Large 4 is listed on OpenRouter as a frontier multimodal model accepting text and image input. The listing says it is built for reasoning, coding, and agentic workloads and offers a 1M-token context window. The feed excerpt is truncated, so further details such as pricing or availability are not confirmed here.
Sophia Yang@sophiamyangAI score45AIMistral Large 4 is a 1T-parameter natively multimodal model with 49B active parameters, which the Mistral account says leads open-weights models from the US or Europe on aggregated benchmarks. The post claims it beats closed frontier models on visual grounding and posts strong results across cybersecurity, legal, and agentic behavior. It is available via API now, with open weights due at the end of October.
Julien Chaumond@julien_cPickAI score70AIJulien Chaumond reposted Mistral's announcement of Mistral Large 4, a 1T-parameter natively multimodal model with 49B active parameters. Mistral says it is available via API today, with open weights scheduled for release at the end of October, and is working privately with cybersecurity partners.
Why it matters: The post lays out Mistral Large 4's scale, multimodal design, and availability timeline, which helps readers gauge the open-weights landscape outside China.
Arthur Mensch@arthurmenschAI score48AIArthur Mensch says Mistral trained its model on its own compute, and reinforcement learning shows no sign of saturating. The post accompanies Mistral's announcement of Mistral Large 4, a 1T-parameter natively multimodal model with 49B active parameters, available via API today and with open weights planned for end of October.
Guillaume Lample @ NeurIPS 2024@GuillaumeLampleAI score62AIGuillaume Lample announced that Mistral's Science team has shipped ML4, which the post links to the Mistral Large 4 news page. He said more is coming soon and that the team is scaling alongside its compute, with hiring open in Europe, the US, and Montreal for frontier open-weight models and large-scale RL systems.
Guillaume Lample @ NeurIPS 2024@GuillaumeLampleAI score40AIMistral says its ML4 model reaches state-of-the-art performance among open models across a wide range of capabilities, and outperforms the best models in visual grounding, legal, and spreadsheet manipulation. The post reports ML4 ranks among the best on the AA Cyber Index, scoring 82% on vulnerability reproduction and patching and 93% on Cybench. It argues that self-hosted, auditable open models are the best defense option for enterprises today, and that they do not refuse to help.
Guillaume Lample @ NeurIPS 2024@GuillaumeLampleAI score26AIMistral says its ML4 model was trained on 3,800 NVIDIA Grace Blackwell GPUs in its European datacenters, including its Bruyères-le-Châtel cluster built with Series B funding. The company is investing further, with Series C and D clusters coming online soon to support longer training, more ambitious post-training, and faster iteration. Mistral expects large and rapid improvements in the weeks and months ahead.
Guillaume Lample @ NeurIPS 2024@GuillaumeLamplePickAI score78AIMistral has launched a preview of Mistral Large 4 (ML4), a 1T-parameter multimodal model with 49B active parameters. The company says it is the strongest open-weight model from the US or Europe on aggregated benchmarks and is available via API now, with open weights planned for the end of October.
Why it matters: The post gives parameter counts, a preview timeline, and an open-weights release date, which help readers judge how Mistral's model compares with other open-weight options.
Mistral AI@MistralAIAI score62AIMistral AI introduced Mistral Large 4, a natively multimodal model with 1T parameters and 49B active parameters. The company says it is the best open-weights model from the US or Europe on aggregated benchmarks and is available via API today, with open weights due at the end of October.
AIMistral AI launched a public preview API for Mistral Large 4, a 1 trillion-parameter natively multimodal model with 52 billion active parameters, and says it will release the weights by the end of the month. The company reports 61.7% on DeepSWE v1.1, 59.4% on SWE-Atlas-QnA, 28.3% on Terminal-Bench 4, and 59.9% on AutomationBench. The model was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's datacenters in Europe.
Why it matters: The post gives benchmark figures and a weights timeline for an open-weight model, letting readers compare it with other open models and judge its access terms.
AIReflection announced Beam, a text-only 501B-total, 23B-active MoE model for coding, agentic, and scientific work, trained from scratch with full weights under Apache 2.0 promised this month. Self-reported results include 80.9 on SWE-bench Verified and 3–4x the inference efficiency of GLM 5.2, while the roundup notes that GLM 5.3, Kimi K3, Qwen 3.8 Max, and DeepSeek V4.1 Flash are generally ahead.
AIMistral has released Mistral Large 4 in Research Public Preview, with open weights for the 1T parameter (49B active) model planned for the end of October. It scores 38 on the Artificial Analysis Intelligence Index, comparable to GPT-6 Luna (max, 38) and DeepSeek V4.1 Flash (max, 39), and 50 on the Cyber Index. The source calls it the most intelligent model from outside the US and China, and notes costs of $1.13 per Intelligence Index task at standard pricing.
AIGoogle has made Gemini Nano Banana 2.1, identified as gemini-nano-banana-2.1, generally available as an image generation and conversational editing model. It improves visual quality, prompt adherence, multi-turn character consistency, and text rendering, and adds panoramic aspect ratios such as 1:4, 4:1, 1:8, and 8:1 at 1K, 2K, and 4K resolutions. The gemini-3.1-flash-image model is deprecated with no shutdown date announced, and developers are told to migrate to the new model.
AIOpenAI says it will ship one meaningful Codex and Work improvement each day for 28 days starting October 5, or else offer a "reset" without specifying what that reset covers. The company also plans to test visual ads in ChatGPT image generation in the U.S. starting in late October, with ads kept separate from generated images and not affecting answers.
Z.ai@Zai_orgAI score36AIZhipu GLM's GLM-5.3 is now available on Amazon Bedrock, offering enterprises coding and agentic capabilities. The post points readers to the Amazon Bedrock model card for GLM-5.3 for getting-started details.
AIReflection AI, an Nvidia-backed startup, released Beam, its first open-weight large model, aimed at coding and agent tasks. The company says Beam is comparable to Z.ai's GLM-5.2 and is approaching Qwen3.8-Max on coding and agent work. Beam has 501 billion total parameters, with 23 billion activated per task in a sparse architecture.
AINikkei found the average gap between upgraded high-performance model releases among five US and four Chinese developers fell from 125 days (January 2023 to March 2026) to 44 days (April to September 2026). Anthropic said its Claude AI led 26% of its R&D efforts as of August and was involved in more than 90% of R&D activities, while OpenAI reported AI agents working more hours than human researchers in August.
AIGoogle DeepMind launched EmbeddingGemma 2, an open-weight 740M parameter model that maps text, images, video frames, and audio into one vector space. The model can run on-device, with about 567MB active RAM for the full multimodal model on a Google Pixel 11 Pro, and is available through Google AI Edge Gallery, Google AI Edge Foresight on Mac, and MediaPipe Tasks, with ML Kit support coming in the weeks ahead.
Why it matters: The post names concrete on-device apps, memory footprints, and latency figures, showing how a multimodal embedding model can power local search without cloud calls.
Together AI@togethercomputeAI score46AIReflection AI has introduced Beam, an open agentic model with 501B total parameters and 23B active, trained end-to-end from scratch. Full weights are slated for release this month. Together AI congratulated the team and is hosting a NYC meet-up with Reflection and NVIDIA next week.
meng shao@shao__mengAI score47AIReflection AI previewed Beam, an MoE open model with 501B total and 23B active parameters, claiming 3–4x better inference efficiency than GLM 5.2. The model was pretrained from scratch on 23.8T tokens in four weeks, and its RL run used 10,500 GB300 GPUs over four weeks, which the post describes as possibly the largest publicly recorded. Reflection positions Beam as a workhorse open model for enterprises, governments, and developers, with full weights due this month.
A.I.Warper@AIWarperAI score42AITencent has open-sourced a new video model called Prism, according to the post. The poster says the previews look solid, with a Hugging Face link provided for access.
Dongxi NLP@dongxi_nlpAI score60AIThe author says Beam, a 501B-parameter open model from Reflection AI, comes close to GLM 5.2 in capability but trails GLM 5.3, Kimi K3, and DeepSeek V4.1 Flash in several areas. The author attributes Beam's competitiveness mainly to inference efficiency, with inference compute at roughly one-third to one-quarter of GLM 5.2's.
Nathan Lambert@natolambertAI score29AINathan Lambert notes that Reflection has joined Nvidia and Thinking Machines in releasing their strongest models while trailing Chinese counterparts. He says the clearest takeaway is that Chinese labs are very strong at building LLMs.
Sophia Yang@sophiamyangAI score62AISophia Yang congratulated Reflection AI on Beam, a 501B-parameter open model with 23B active per token. She attributes its efficiency to an RL length penalty that discourages unnecessary tokens and a sparse MoE architecture. Reflection says full weights will be released this month, and the quoted post reports training over 100 million rollouts on 10.5K NVIDIA GB300 GPUs over four weeks.
NVIDIA AI@NVIDIAAIAI score39AINVIDIA has published Nemotron-Labs-3-Competitive-Coding on Hugging Face, a competitive-programming specialist model built on Nemotron-3-Ultra. The model is available in the NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4 repository, indicating a 550B-parameter total size with 55B active parameters in NVFP4 format.
Cline@clineAI score34AICline's Pareto 26.10 Preview routes each prompt to several frontier and open models, grades their answers, and returns the best one while preserving prompt cache. Cline says it matches Fable's DeepSWE score at $0.24 per task versus $13.41, about 56 times cheaper.
clem 🤗@ClementDelanguePickAI score72AIReflection AI introduced Beam, an agentic open model with 501B total parameters and 23B active parameters, trained end-to-end from scratch. The quoted announcement says it targets frontier reasoning efficiency and coding and agentic tasks, with full weights due this month. Clément Delangue, Hugging Face's CEO, reposted it with a welcome to the Reflection organization on Hugging Face.
Why it matters: The quoted announcement names Beam's parameter scale, active-parameter count, and coding and agentic focus, which helps readers gauge where it fits among open models.
ollama@ollamaAI score46AIOllama says it is excited about a new open model from Reflection AI and that more US models are coming to Ollama. Reflection AI's Beam is described as an agentic open model with 501B total parameters and 23B active, with full weights due this month.
Pika@pika_labsAI score22AIPika announces that users can try Ideogram 4.5 through its create platform, linking to an Ideogram app page in its image tools section. The post provides no details on features, pricing, or capabilities.
Nathan Lambert@natolambertAI score43AIReflection AI has introduced Beam, an open agentic model with 501B total parameters and 23B active, trained end-to-end from scratch. The company says Beam advances the Western open frontier on coding and agentic tasks, with full weights set for release this month.
Reflection@reflection_aiAI score23AIReflection AI says its Beam model was pretrained in 4 weeks on 24T high-quality tokens, giving it innate coding capabilities. The company credits MoE stability improvements and large-scale data curation and deduplication for a base model it claims outperforms open-source base models of the same class. It presents this strong reasoning foundation as what makes sustained reinforcement learning gains possible.
Reflection@reflection_aiAI score42AIReflection AI says its Beam model, with a 500B form factor, combines strong agentic performance and efficient reasoning for enterprises, governments, and developers. Beam is in final red-teaming and will be released this month under an Apache 2.0 license, with quantized FP8 and NVFP4 versions for efficient deployment. Early access sign-ups are open on the company's platform.
Alex Heath@alexeheathAI score52AIReflection is set to release Beam, its first open-weight AI model, aiming to become a Western counterpart to DeepSeek. The source says Beam is trained from scratch for coding, reasoning, and AI agents, with benchmarks placing it alongside the strongest open models and more efficient token economics. Reflection has raised $4.6 billion from investors including Nvidia, Sequoia, and Lightspeed, and the interview covers its monetization plans for open-weight models.
Liquid AI@liquidaiAI score37AILiquid AI released d1 with vision support, accepting images, text, or both as inputs. In tests on six real applications, d1 matched or beat GPT-6.1 Sol on four while costing 19x to 200x less than both GPT-6.1 Sol and Claude Opus 5.5. It returns probabilities for yes/no, choice, or score questions in one forward pass, with text decisions in 200 to 300 ms.
Liquid AI@liquidaiAI score36AILiquid AI's vision-enabled decision model d1 inspects parts directly from camera images and is described as the best such model currently on the market. It reaches 85% to 97% accuracy across four VisA inspection tasks covering circuit boards, candles, cashews, and chewing gum. It understands each task from a short description without task-specific training.
AILiquidAI has released d1-omni-600M on Hugging Face, a 587M-parameter model that answers named yes/no, choice and score questions over text, images or up to 30 seconds of speech in a single forward pass. It returns typed answers with zero output tokens by reading the model's distribution over options, and is built on LFM2.5-Encoder-350M with a 16,384-token context length. The model is not a chat model and does not generate text.
Google Antigravity@antigravityAI score20AIGoogle Antigravity shared a demo of a mini self-generating forest made entirely of colorful pencil strokes, built with Gemini 3.8 from a sketch reference and a prompt. The quoted post says the result came from turning an art sketch into an exploratory mini game.